Introduction
In the previous chapter, we learned how to perform the first steps in any statistical analysis. Given a business or scientific problem and a related dataset, we learned how to load the dataset and prepare it for analysis. Then, we learned how to calculate and use descriptive statistics to make sense of the variables. Finally, we performed EDA to complement the information we gathered from the descriptive statistics and gained a better understanding of the variables and their possible relationships. After getting a basic understanding of an analytical problem, you may need to go one step further and use more sophisticated quantitative tools, some of which are used in the following fields:
- Inferential statistics
- Machine learning
- Prescriptive analytics
- Optimization
What do all of these domains have in common? Many things: for example, they have a mathematical nature, they make heavy use of computational tools, and in one way or another they use...
